Principal Data Scientist

• Proficiency with Python and experience with data processing tools (e.g., scipy, numpy, pandas, Pytorch, and Spark)
• Understand techniques required to automate tuning of forecasting and anomaly detection algorithms, ideally using ML as a foundation
• Be proficient in deploying models to scalable pipelines for multi-tenant cloud SaaS software
• Excited to make customers successful with the deployed models and feel pride of ownership in the solutions enabled by your work
• Give talks, write blog posts, and produce peer-reviewed publications
• MS or PhD in engineering, statistics, economics, or related field with 3+ (PhD) or 5+ (MS) years of experience outside of education
• Strong technical communication skillsNice to Have
• Past experience working with business metrics and interacting with business/non-technical stakeholders
• Preference for, and experience with, building interpretable, explainable models that deliver insight to end users
• Experience with full-stack machine learning development - from data intake and processing to model development to testing and validation in production
• Structured approach to research and development - willingness to get to a workable implementation quickly today while building towards a more comprehensive solution in the future
• Be familiar with AWS and potentially also GCP and Azure as it applies to data science deployments
• Understand architectural and compute cost implications of ML techniquesTech stack
• NumPy, SciPy, Stan, Plotly, Matplotlib
• SQL, DBT, Airflow, Spark